---
title: FunctionGemma 270M SAE
language: en
tags:
  - sparse-autoencoder
  - sae
  - interpretability
  - functiongemma
  - gemma
license: apache-2.0
---

# FunctionGemma 270M Sparse Autoencoders

Sparse Autoencoders (SAEs) trained on all 18 layers of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it).

## Architecture

- **Base Model**: google/functiongemma-270m-it
- **Layers**: 18 (decoder-only)
- **Hidden Size**: 640
- **SAE Dimension**: 4096 (6.4x expansion)
- **Hook Point**: `self_attn.o_proj` (output projection of self-attention)

## Training

- **Epochs**: 5 per layer
- **Batch Size**: 1
- **Learning Rate**: 1e-4
- **Optimizer**: AdamW
- **Loss**: MSE + 0.01 * L1 regularization
- **Activation Clipping**: [-10, 10]
- **Gradient Clipping**: max_norm=1.0

## Checkpoints

Each checkpoint contains:
```python
{
    "model_name": "google/functiongemma-270m-it",
    "layer_idx": int,
    "d_in": 640,
    "d_sae": 4096,
    "W_enc": torch.Tensor,  # (640, 4096)
    "b_enc": torch.Tensor,  # (4096,)
    "W_dec": torch.Tensor,  # (4096, 640)
    "b_dec": torch.Tensor,  # (640,)
    "history": {
        "loss": [...],
        "mse": [...],
        "l0": [...]
    }
}
```

## Usage

```python
import torch
from huggingface_hub import hf_hub_download

# Load SAE for a specific layer
layer_idx = 0
ckpt_path = hf_hub_download(
    "mindchain/functiongemma-270m-sae",
    f"sae_layer_{layer_idx:02d}.pt"
)
sae = torch.load(ckpt_path, map_location="cpu")

# Use SAE
class JumpReLUSAE(torch.nn.Module):
    def __init__(self, W_enc, b_enc, W_dec, b_dec):
        super().__init__()
        self.W_enc = torch.nn.Parameter(W_enc)
        self.b_enc = torch.nn.Parameter(b_enc)
        self.W_dec = torch.nn.Parameter(W_dec)
        self.b_dec = torch.nn.Parameter(b_dec)

    def forward(self, x):
        batch, seq, d_in = x.shape
        x_flat = x.view(-1, d_in)
        pre_act = x_flat @ self.W_enc + self.b_enc
        features = torch.relu(pre_act)
        recon = features @ self.W_dec + self.b_dec
        return recon.view(batch, seq, d_in), features.view(batch, seq, -1)

sae_model = JumpReLUSAE(
    sae["W_enc"], sae["b_enc"],
    sae["W_dec"], sae["b_dec"]
)

# Get activations from FunctionGemma and encode
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "google/functiongemma-270m-it",
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)
tokenizer = AutoTokenizer.from_pretrained("google/functiongemma-270m-it")

inputs = tokenizer("What's the weather?", return_tensors="pt").to(model.device)

# Hook to get activations
acts = []
def hook(module, inp, out):
    acts.append(out[0].detach().float())
handle = model.model.layers[layer_idx].self_attn.o_proj.register_forward_hook(hook)
with torch.no_grad():
    _ = model(**inputs)
handle.remove()

# Run through SAE
recon, features = sae_model(acts[0])
print(f"Active features: {(features > 0).sum().item()}")
```

## Training Results

| Layer | Final Loss | Final MSE | L0 |
|-------|------------|-----------|-----|
| 0 | 3.4457 | 3.1244 | 1225 |
| 1 | 2.0052 | 1.9042 | 1386 |
| 2 | 0.1182 | 0.0759 | 1546 |
| 3 | 0.1182 | 0.0758 | 3096 |
| 4 | 0.0361 | 0.0170 | 1635 |
| 5 | 0.0414 | 0.0351 | 399 |
| 6 | 0.0318 | 0.0138 | 1807 |
| 7 | 0.0877 | 0.0661 | 1120 |
| 8 | 0.0733 | 0.0445 | 1379 |
| 9 | 0.0561 | 0.0317 | 1569 |
| 10 | 0.0997 | 0.0852 | 591 |
| 11 | 0.0252 | 0.0097 | 3658 |
| 12 | 0.0565 | 0.0395 | 962 |
| 13 | 0.0924 | 0.0619 | 1403 |
| 14 | 0.2711 | 0.2504 | 709 |
| 15 | 0.1501 | 0.1062 | 1576 |
| 16 | 0.1670 | 0.1426 | 870 |
| 17 | 0.0385 | 0.0218 | 1470 |

## License

Apache 2.0